Evaluating tag-reliant harvest estimators in Chinook salmon mixed-stock fisheries using simulations
Bibliographic record
Abstract
Management of mixed-stock Chinook salmon (Oncorhynchus tshawytscha) fisheries requires balancing fishery access and conservation of vulnerable stocks. Although accurate, timely estimates of stock-specific harvest are crucial in achieving competing objectives, limited numbers of stock assignments (e.g., tag recoveries) can diminish the utility of estimates. We used a flexible simulation approach, applied to both a theoretical and real-world fishery case study, to compare the performance of competing monitoring alternatives and estimators for harvest. We sought to improve accuracy for point estimates of harvest and harvest trajectories over time. Bayesian models provided similarly accurate point estimates to existing models at high levels of data aggregation, generally improved estimates of harvest trajectories at intermediate aggregation, and reliable estimates of uncertainty. Incorporation of time-lagged prior information inconsistently improved estimates of harvest trajectories. Among monitoring alternatives yielding equal increases (33%) in coded wire tag recoveries, increasing tagging rates resulted in the greatest decrease in estimate uncertainty for target stocks (37.5% to 45.3%). Variable performances of mixed-stock harvest estimators suggest their use should considered on a stock- and fishery-specific basis, potentially using a simulation-based approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".